Breeding better crops and livestock now depends on computer simulations that predict how genetic changes will play out in the real world, but those simulations cannot yet handle the full complexity of real genomes or the environments where animals and plants must grow. Current simulation software struggles with polyploid crops like wheat and potato—which carry multiple sets of chromosomes—and cannot easily model how traits like drought resilience or disease resistance interact with changing climates. This project will stitch together two existing simulation packages, AlphaSimR and msprime, using a data structure called tree sequences that efficiently encodes the entire genetic history of a population. The team will also build a new tool, PhenoSimR, to simulate complex traits that respond to environmental variation. Finally, they will extract tree sequences from publicly available genomic data for six major agricultural species—wheat, potato, maize, cattle, sheep, and pig—and make them freely accessible. If successful, breeders could test new selection strategies in silico before committing to expensive field trials, accelerating genetic gains for food security. The tools will also unlock underused public genomic data, turning it into a practical resource for both academic research and commercial breeding programmes.
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This project will enhance the agrigenetic simulation ecosystem to supercharge theoretical and applied studies in selective breeding and the adoption of their results in practice. Similar to other branches of science, simulations are pivotal to research in modern data-driven selective breeding of agricultural populations. These simulations are also an essential tool for decision making in applied breeding; on how to deploy new data generation processes and associated data analysis methods to increase the efficiency and return on investment. These uses of simulation have a significant academic, industrial, and societal impact by advancing the fundamental role of agriculture – food production. There is now a vibrant and complementary ecosystem of high-quality simulation software packages. While some of the software packages are already interoperable, further developments are required to increase interoperability and to fully leverage their distinct strengths. Specifically, we need three key innovations to improve the agrigenetic simulation ecosystem. First, we need to increase interoperability between population genomics and selective breeding simulation software packages by leveraging the succinct tree sequence encoding of ancestral recombination graphs, including support for the common diploid genomes and complex polyploid genomes. Second, we need to increase flexibility of agrigenetic simulation software to generate complex phenotypes as a function of a range of effects with user-defined relationships to account for the nature of agriculturally important traits and concepts such as adaptability, resilience and genotype-by-environment interactions. Third, we need to increase the use of vast amounts of publicly available genomic data in an easy-to-use and affordable way. This project will deliver these needs through three work packages: i) Supercharging genetic and selective breeding simulations with tree sequences, ii) Simulating complex phenotypes that capture environmental variation and its interaction, and iii) Accessible genomic and phenotypic data resources for agriculture. Through these work packages we will extend the software packages AlphaSimR and msprime to increase interoperability of genomic data and add support for complex polyploid genomes, all via tree sequences. We will further develop a flexible PhenoSimR package to create a general framework for simulating complex phenotypes. Finally, we will infer tree sequences from publicly available genome-wide genotype and whole-genome sequence data of six key agricultural species (wheat, potato, maize, cattle, sheep, and pig). We will host these tree sequences on a server and create deployable server containers to offer an accessible live demonstration of the developed tools from this project. This project will enhance a popular agrigenetic simulation ecosystem and will further boost its uptake in applied settings and academia. Project development was guided by significant community demand. The work will be undertaken with the advice of an expert board and supported by our project partners. This community is collectively delivering significant academic outputs and a majority of the UK and world-wide genetic gains through selective breeding of their populations. There is an urgent need for the development of the proposed tools due to the growing world population and climate change, both increasing pressure on agriculture to produce more food with greater efficiency and sustainability. The cutting-edge results from this project will expand the ecosystem of agrigenetic simulations to contribute to solving these challenges with data-driven approaches. The project will also promote use of publicly available genomic data that has to date been underutilised. These results provide the essential springboard to develop future digital twins for selective breeding programmes.
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